Intelligent Transportation Systems

2. The method of claim 1 wherein the controlling at least one aspect of the vehicle is performed via the vehicle control-facilitating interface.

3. The method of claim 1 wherein the controlling at least one aspect of the vehicle is performed by the artificial intelligence system emulating the control-facilitating interface being operated by the human.

4. The method of claim 1 wherein the vehicle control-facilitating interface comprises at least one of an audio capture system to capture audible expressions of the human, a human-machine interface, a mechanical interface, an optical interface and a sensor-based interface.

5. The method of claim 1 wherein the tracking vehicle operational state information comprises tracking at least one of a set of vehicle systems and a set of vehicle operational processes affected by the human interactions.

6. The method of claim 1 wherein the tracking vehicle operational state information comprises tracking at least one vehicle system element, wherein the at least one vehicle system element is controlled via the vehicle control-facilitating interface, and wherein the at least one vehicle system element is affected by the human interactions.

7. The method of claim 1 wherein the tracking vehicle operational state information comprises tracking the vehicle operational state information before, during, and after the human interaction.

8. The method of claim 1 wherein the tracking vehicle operational state information comprises tracking at least one of a plurality of vehicle control system outputs that result from the human interactions and vehicle operational results achieved in response to the human interactions.

9. The method of claim 1 wherein the vehicle is to be controlled to achieve results that are consistent with results achieved via the human interactions.

10. The method of claim 1 further comprising tracking and recording conditions proximal to the vehicle with a plurality of vehicle mounted sensors, wherein the training of the artificial intelligence system is further responsive to the conditions proximal to the vehicle tracked contemporaneously to the human interactions.

11. The method of claim 10 wherein the training is further responsive to a plurality of data feeds from remote sensors, the plurality of data feeds comprising data collected by the remote sensors contemporaneous to the human interactions.

12. The method of claim 1 wherein the artificial intelligence system employs a workflow that involves decision-making and the robotic process automation system facilitates automation of the decision-making.

13. The method of claim 1 wherein the artificial intelligence system employs a workflow that involves remote control of the vehicle and the robotic process automation system facilitates automation of remotely controlling the vehicle.

15. The transportation system of claim 14 wherein the operator data collection module is to capture patterns of data including braking patterns, follow-behind distance, approach to curve acceleration patterns, lane preferences, and passing preferences.

16. The transportation system of claim 14 wherein vehicle data collection module captures data from a plurality of vehicle data systems that provide data streams indicating states and changes in state in steering, braking, acceleration, forward looking images, and rear-looking images.

17. The transportation system of claim 14 wherein the artificial intelligence system includes a neural network for training the artificial intelligence system.

19. The method of claim 18 wherein the robotic process automation system facilitates automation of a decision-making workflow employed by the artificial intelligence system.

20. The method of claim 18 wherein the robotic process automation system facilitates automation of a remote control workflow that the artificial intelligence system employs to remotely control the vehicle.

21. The method of claim 1 wherein the at least one aspect of the vehicle includes at least one of a climate control system, a combustion engine system, an electric vehicle drive train system, a powertrain system, a seats system, a suspension system, or a transmission system.

22. The method of claim 1 wherein the structured variation is rule-based variation of the controlling the at least one aspect of the vehicle, wherein the deep learning is to improve outcomes associated with the acquired skills to operate the vehicle in a manner consistent with the human interactions.

23. The method of claim 14 wherein the structured variation is rule-based variation of the learned skills that mimic the human operator, wherein the deep learning is to improve outcomes associated with the learned skills to mimic the human operator to control the vehicle.

24. The method of claim 18 wherein the structured variation is rule-based variation of the controlling of the vehicle, wherein the deep learning is to improve outcomes associated with the acquired skills to control the vehicle mimicking the human operator.